A structural systems biology approach for quantifying the systemic consequences of missense mutations in proteins

Gauging the systemic effects of non-synonymous single nucleotide polymorphisms (nsSNPs) is an important topic in the pursuit of personalized medicine. However, it is a non-trivial task to understand how a change at the protein structure level eventually affects a cell's behavior. This is because complex information at both the protein and pathway level has to be integrated. Given that the idea of integrating both protein and pathway dynamics to estimate the systemic impact of missense mutations in proteins remains predominantly unexplored, we investigate the practicality of such an approach by formulating mathematical models and comparing them with experimental data to study missense mutations. We present two case studies: (1) interpreting systemic perturbation for mutations within the cell cycle control mechanisms (G2 to mitosis transition) for yeast; (2) phenotypic classification of neuron-related human diseases associated with mutations within the mitogen-activated protein kinase (MAPK) pathway. We show that the application of simplified mathematical models is feasible for understanding the effects of small sequence changes on cellular behavior. Furthermore, we show that the systemic impact of missense mutations can be effectively quantified as a combination of protein stability change and pathway perturbation

Mining protein loops using a structural alphabet and statistical exceptionality

<p>Abstract</p> <p>Background</p> <p>Protein loops encompass 50% of protein residues in available three-dimensional structures. These regions are often involved in protein functions, e.g. binding site, catalytic pocket... However, the description of protein loops with conventional tools is an uneasy task. Regular secondary structures, helices and strands, have been widely studied whereas loops, because they are highly variable in terms of sequence and structure, are difficult to analyze. Due to data sparsity, long loops have rarely been systematically studied.</p> <p>Results</p> <p>We developed a simple and accurate method that allows the description and analysis of the structures of short and long loops using structural motifs without restriction on loop length. This method is based on the structural alphabet HMM-SA. HMM-SA allows the simplification of a three-dimensional protein structure into a one-dimensional string of states, where each state is a four-residue prototype fragment, called structural letter. The difficult task of the structural grouping of huge data sets is thus easily accomplished by handling structural letter strings as in conventional protein sequence analysis. We systematically extracted all seven-residue fragments in a bank of 93000 protein loops and grouped them according to the structural-letter sequence, named structural word. This approach permits a systematic analysis of loops of all sizes since we consider the structural motifs of seven residues rather than complete loops. We focused the analysis on highly recurrent words of loops (observed more than 30 times). Our study reveals that 73% of loop-lengths are covered by only 3310 highly recurrent structural words out of 28274 observed words). These structural words have low structural variability (mean RMSd of 0.85 Å). As expected, half of these motifs display a flanking-region preference but interestingly, two thirds are shared by short (less than 12 residues) and long loops. Moreover, half of recurrent motifs exhibit a significant level of amino-acid conservation with at least four significant positions and 87% of long loops contain at least one such word. We complement our analysis with the detection of statistically over-represented patterns of structural letters as in conventional DNA sequence analysis. About 30% (930) of structural words are over-represented, and cover about 40% of loop lengths. Interestingly, these words exhibit lower structural variability and higher sequential specificity, suggesting structural or functional constraints.</p> <p>Conclusions</p> <p>We developed a method to systematically decompose and study protein loops using recurrent structural motifs. This method is based on the structural alphabet HMM-SA and not on structural alignment and geometrical parameters. We extracted meaningful structural motifs that are found in both short and long loops. To our knowledge, it is the first time that pattern mining helps to increase the signal-to-noise ratio in protein loops. This finding helps to better describe protein loops and might permit to decrease the complexity of long-loop analysis. Detailed results are available at <url>http://www.mti.univ-paris-diderot.fr/publication/supplementary/2009/ACCLoop/</url>.</p

Erratum: The solar orbiter radio and plasma waves (RPW) instrument (Astronomy and Astrophysics (2020) 642 (A12) DOI: 10.1051/0004-6361/201936214)

The erratum concerns Fig. 9 entitled "Antenna radio-electrical properties" for which some of the parameters are not correct. The new figure with new parameters is provided in Fig. 1 of this corrigendum. Fig. 1. Corrected Antenna radio-electrical properties. (Figure Presented)

Trends in template/fragment-free protein structure prediction

Predicting the structure of a protein from its amino acid sequence is a long-standing unsolved problem in computational biology. Its solution would be of both fundamental and practical importance as the gap between the number of known sequences and the number of experimentally solved structures widens rapidly. Currently, the most successful approaches are based on fragment/template reassembly. Lacking progress in template-free structure prediction calls for novel ideas and approaches. This article reviews trends in the development of physical and specific knowledge-based energy functions as well as sampling techniques for fragment-free structure prediction. Recent physical- and knowledge-based studies demonstrated that it is possible to sample and predict highly accurate protein structures without borrowing native fragments from known protein structures. These emerging approaches with fully flexible sampling have the potential to move the field forward

Mutations in mammalian tolloid-like 1 gene detected in adult patients with ASD

Atrial septal defect (ASD) is an incomplete septation of atria in human heart causing circulatory problems. Its frequency is estimated at one per 10 000. Actions of numerous genes have been linked to heart development. However, no single gene defect causing ASD has yet been identified. Incomplete heart septation similar to ASD was reported in transgenic mice with both inactive alleles of gene encoding mammalian zinc metalloprotease a mammalian tolloid-like 1 (tll1). Here, we have screened 19 ASD patients and 15 healthy age-matched individuals for mutations in TLL1 gene. All 22 exons were analyzed exon by exon for heteroduplex formation. Subsequently, DNA fragments forming heteroduplexes were sequenced. In four nonrelated patients, three missense mutations in coding sequence, and one single base change in the 5′UTR have been detected. Two mutations (Met182Leu, and Ala238Val) were detected in ASD patients with the same clinical phenotype. As the second mutation locates immediately upstream of the catalytic zinc-binding signature, it might change the enzyme substrate specificity. The third change, Leu627Val in the CUB3 domain, has been found in an ASD patient with interatrial septum aneurysm in addition to ASD. The CUB3 domain is important for substrate-specific recognition. In the remaining 15 patients as well as in 15 reference samples numerous base substitutions, deletions, and insertions have been detected, but no mutations changing the coding sequence have been found. Lack of mutations in relation to ASD of these patients could possibly be because of genetic heterogeneity of the syndrome